My bot for interview prep saved me 10 hours last month. Prompt shared in comments.

I've been conducting a series of technical interviews for new backend engineering candidates, and the repetitive nature of initial screening was consuming an inordinate amount of my time. Crafting relevant questions, evaluating basic code submissions, and providing consistent, actionable feedback for each applicant created a significant overhead. To streamline this, I built a Poe bot that handles the first-round, take-home coding assessment, freeing me to focus on deeper, interactive discussions in later stages.

The bot's architecture is centered around a structured, multi-step interaction that mimics a fair and consistent initial screen. It presents a problem, accepts code via a message or paste, performs a basic analysis, and then provides a guided review. The key was constructing a prompt that enforces a specific rubric and engages the candidate in a quasi-interactive manner before delivering a summary to me. Below is the core system prompt that defines the bot's behavior.

```markdown
You are an automated initial screening assistant for backend engineering interviews. Your task is to evaluate a candidate's submission for a specific problem. Follow this sequence strictly:

1. **Problem Presentation:** Begin by presenting the following problem clearly and concisely. Do not proceed until the candidate indicates they are ready or submits code.
* Problem: "Design a simple API rate limiter for an imaginary service. The system should allow a maximum of N requests per client (identified by an API key) over a sliding window of T seconds. Provide a code outline or pseudocode in a language of your choice, focusing on the data structures and algorithm logic. Assume a distributed environment is not required for this initial design."

2. **Code Collection & Acknowledgment:** Once code is received, acknowledge it. Ask one clarifying question about their design choice (e.g., "Why did you choose a particular data structure for tracking request timestamps?").

3. **Structured Analysis:** After they answer, provide your analysis in this exact format:
**Conceptual Understanding:** [Brief note on correctness of the core idea]
**Technical Soundness:** [Note on data structure choices and algorithm efficiency]
**Communication:** [Note on code clarity and response to the follow-up question]
**Overall Assessment:** [One of: Strong / Proceed with Caution / Not a Fit]

4. **Final Summary for Recruiter:** Generate a final, compact summary block for the human interviewer, aggregating the above points into a single paragraph.
```

The bot is configured with the following settings:
- **Base bot:** ChatGPT
- **Prompt type:** System prompt (as above)
- **Introduction message:** "Hello, I'll be guiding you through the initial coding assessment. Please let me know when you are ready to receive the problem statement."

In practice, this workflow has automated approximately 80% of the initial screening effort. The bot consistently applies the same criteria, and the "Final Summary for Recruiter" provides me with a standardized note that I can quickly review before deciding on a candidate's progression. The most significant time savings came from eliminating the need to manually set up the problem, collect code from emails, and write the first-pass evaluation for every single submission.

Potential pitfalls to consider:
- The bot cannot judge truly novel or creative solutions with the nuance of a human; it's best for filtering out clearly insufficient submissions.
- You must ensure the problem is scoped correctly for an automated screen—conceptual design over full implementation works best.
- The follow-up question is crucial, as it forces a small interactive element that reveals the candidate's ability to articulate their reasoning, which the bot then assesses under "Communication."

This approach has proven effective for screening fundamental knowledge. For the next iteration, I am considering integrating a simple webhook from the bot's endpoint to automatically post the final summary to a private channel in our internal communication platform, further reducing context-switching. The return on investment in terms of hours saved has been substantial, allowing for a more focused and in-depth interview process later on.